Using cyclic markov decision process to determine optimum policy

ABSTRACT

A method for determining an optimum policy by using a Markov decision process in which T subspaces each have at least one state having a cyclic structure includes identifying, with a processor, subspaces that are part of a state space; selecting a t-th (t is a natural number, t≦T) subspace among the identified subspaces; computing a probability of, and an expected value of a cost of, reaching from one or more states in the selected t-th subspace to one or more states in the t-th subspace in a following cycle; and recursively computing a value and an expected value of a cost based on the computed probability and expected value of the cost, in a sequential manner starting from a (t −1)-th subspace.

PRIORITY

This application is a continuation of U.S. Ser. No. 13/586,385, filedAug. 15, 2012, which claims priority to Japanese Patent Application No.2011-218556, filed 30 Sep. 2011, and all the benefits accruing therefromunder 35 U.S.C. §119, the contents of which in its entirety are hereinincorporated by reference.

BACKGROUND

The present disclosure relates to a method, an apparatus, and a computerprogram for using a cyclic Markov decision process to determine, withreduced computational processing load, an optimum policy that minimizesan average cost.

A method of solving a control problem that is formulated as theso-called “Markov decision process” is one of the techniques that can beapplied to a wide variety of fields, such as robotics, power plants,factories, and railroads, to solve autonomous control problems in thosefields. In a “Markov decision process”, a control problem of a statetransition that is dependent on time of an event is solved by using thedistance (cost) from an ideal state transition as the evaluationcriterion.

For example, JP2011-022902A discloses an electric power transactionmanagement system which manages automatic electric power interchange atpower generating and power consumption sites such as minimal clustersincluding equipment, such as a power generator, an electric storagedevice, and electric equipment, and power router and uses a Markovdecision process to determine an optimum transaction policy.JP2005-084834A discloses an adaptive controller that uses a Markovdecision process in which a controlled device transitions to the nextstate according to a state transition probability distribution. Thecontroller is thus caused to operate as a probabilistic controller inorder to reduce the amount of computation in algorithms such as dynamicprogramming algorithms in which accumulated costs are computed andexhaustive search algorithms in which a policy is directly searched for.

Other approaches that use Markov decision processes to determine optimumpolicies include value iteration, policy iteration, and so-called linearprogramming, which is disclosed in JP2011-022902A. In the case of aMarkov decision process that has a special structure, the specialstructure itself is used to efficiently determine an optimum policy asdisclosed in JP2005-084834A.

SUMMARY

In one embodiment, a method for determining an optimum policy by using aMarkov decision process in which T subspaces each have at least onestate having a cyclic structure includes identifying, with a processor,subspaces that are part of a state space; selecting a t-th (t is anatural number, t≦T) subspace among the identified subspaces; computinga probability of, and an expected value of a cost of, reaching from oneor more states in the selected t-th subspace to one or more states inthe t-th subspace in a following cycle; and recursively computing avalue and an expected value of a cost based on the computed probabilityand expected value of the cost, in a sequential manner starting from a(t−1)-th subspace.

In another embodiment, an apparatus for determining an optimum policy byusing a Markov decision process in which T subspaces each have at leastone state having a cyclic structure includes a processor implementedsubspace identifying unit that identifies subspaces that are part of astate space; a processor implemented election unit that selects the t-th(t is a natural number, t≦T) subspace among the identified subspaces; aprocessor implemented probability and cost computing unit that computesa probability of, and an expected value of a cost of, reaching from oneor more states in the selected t-th subspace to one or more states inthe t-th subspace in a following cycle; and a processor implementedrecursive computing unit that recursively computes a value and anexpected value of a cost based on the computed probability and expectedvalue of the cost, in a sequential manner starting from a (t−1)-thsubspace.

In another embodiment, a computer program product includes a computerreadable storage medium having computer readable code stored thereonthat, when executed by a computer, implement a method for determining anoptimum policy by using a Markov decision process in which T subspaceseach have at least one state having a cyclic structure. The methodincludes identifying, with a processor, subspaces that are part of astate space; selecting a t-th (t is a natural number, t≦T) subspaceamong the identified subspaces; computing a probability of, and anexpected value of a cost of, reaching from one or more states in theselected t-th subspace to one or more states in the t-th subspace in afollowing cycle; and recursively computing a value and an expected valueof a cost based on the computed probability and expected value of thecost, in a sequential manner starting from a (t−1)-th subspace.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram schematically illustrating a configuration ofan information processing apparatus according to an embodiment of thepresent disclosure;

FIG. 2 is a functional block diagram illustrating the informationprocessing apparatus according to the embodiment of the presentdisclosure;

FIG. 3 is a flowchart of a process procedure performed by a CPU of theinformation processing apparatus according to an embodiment of thepresent disclosure; and

FIG. 4 is a table for comparison of computational processing timesrequired for obtaining an optimum policy using a Markov decision processon the information processing apparatus according to an embodiment ofthe present disclosure.

DETAILED DESCRIPTION

Methods such as value iteration, policy iteration, and linearprogramming have difficulty in application to general problems becausethe size of solvable problems is quite limited. The method using thespecial structure described above is also disadvantageous in thatcomplicated processing for computing an inverse matrix poses a sizeconstraint on problems to which the method is applicable.

The present disclosure provides a method, an apparatus, and a computerprogram for determining an optimum policy with a cyclic Markov decisionprocess more efficiently than existing computing methods.

100151 One embodiment provides a method that determines an optimumpolicy by using a Markov decision process in which T (T is a naturalnumber) subspaces each having at least one state having a cyclicstructure, including: identifying subspaces that are part of a statespace; receiving selection of a t-th (t is a natural number, t≦T)subspace among the identified subspaces; computing a probability of, andan expected value of a cost of, reaching from one or more states in theselected t-th subspace to one or more states in the t-th subspace in afollowing cycle; and recursively computing a Value and an expected valueof a cost based on the computed probability and expected value of thecost, in a sequential manner starting from a (t−1)-th subspace.

A method according to a second embodiment includes a method of selectingof a subspace having fewest states among the T subspaces as the t-thsubspace.

A method according to a third embodiment computes an average value ofvalues and an average value of expected values of cost of one or morestates in the t-th subspace.

A method according to a fourth embodiment computes a value variable foreach of the T subspaces to optimize the Markov decision process.

Furthermore, a fifth embodiment provides an apparatus that determines anoptimum policy by using a Markov decision process in which T (T is anatural number) subspaces each having at least one state having a cyclicstructure, including: a subspace identifying unit that identifiessubspaces that are part of a state space; a selection unit that selectsthe t-th (t is a natural number, t≦T) subspace among the identifiedsubspaces; a probability and cost computing unit that computes aprobability of, and an expected value of a cost of, reaching from one ormore states in the selected t-th subspace to one or more states in thet-th subspace in a following cycle; and a recursive computing unit thatrecursively computes a Value and an expected value of a cost based onthe computed probability and expected value of the cost, in a sequentialmanner starting from a (t−1)-th subspace.

An apparatus according to a sixth embodiment includes a method ofselection of a subspace having fewest states among the T subspaces asthe t-th subspace.

An apparatus according to a seventh embodiment computes an average valueof values and an average value of expected values of cost of one or morestates in the t-th subspace.

An apparatus according to an eighth embodiment computes a value variablefor each of the T subspaces to optimize the Markov decision process.

A ninth embodiment provides a computer program executable in anapparatus that determines an optimum policy by using a Markov decisionprocess in which T (T is a natural number) subspaces each having atleast one state having a cyclic structure, the computer program causingthe apparatus to function as: subspace identifying means for identifyingsubspaces that are part of a state space; selection means for selectingthe t-th (t is a natural number, t≦T) subspace among the identifiedsubspaces; probability and cost computing means for computing aprobability of, and an expected value of a cost of, reaching from one ormore states in the selected t-th subspace to one or more states in thet-th subspace in a following cycle; and recursive computing means forrecursively computing a Value and an expected value of a cost based onthe computed probability and expected value of the cost, in a sequentialmanner starting from a (t−1)-th subspace.

The present embodiments enables a cyclic Markov decision process todetermine optimum policies for problems having large sizes and determineoptimum policies for problems that cannot be solved by conventionalalgorithms such as value iteration, policy iteration, and linearprogramming.

An apparatus using a cyclic Markov decision process to determine anoptimum policy that minimizes an average cost with reduced computationalload according to an embodiment of the present disclosure will bedescribed below in detail with reference to drawings. It will beunderstood that the embodiment described below is not intended to limitthe present disclosure in the scope of claims and that not all of thecombinations of features described in the embodiment are essential tothe solution to the problems.

The present disclosure can be carried out in many different modes andshould not be interpreted as being limited to the specifics of theembodiment described. Throughout the embodiment, like element are givenlike reference numerals.

100271 While an information processing apparatus in which a computerprogram is installed in a computer system will be described with theembodiment given below, part of the present disclosure can beimplemented as a computer-executable computer program as will beapparent to those skilled in the art. Accordingly, the presentdisclosure can be implemented by hardware as an apparatus determining anoptimum policy that minimizes an average cost with a reducedcomputational load for a cyclic Markov decision process, or software, ora combination of software and hardware. The computer program can berecorded on any computer-readable recording medium, such as a hard disk,a DVD, a CD, an optical storage device, or a magnetic storage device.

The embodiments of the present disclosure enable a cyclic Markovdecision process to determine optimum policies for problems having largesizes and determine optimum policies for problems that cannot be solvedby conventional algorithms such as value iteration, policy iteration,and linear programming It should be noted that a Markov decision processis cyclic if a state space can be divided into T subspaces (where T is anatural number) and transition from the t-th subspace (where t is anatural number, t≦T) can be made only to the (t+1)-st subspace using anypolicy, where the (T+1)-st subspace is equal to the first subspace. Inthis case, the Markov decision process is defined as having a cycle withthe length T and the t-th subspace can be translated into the statespace at time t.

FIG. 1 is a block diagram schematically illustrating a configuration ofan information processing apparatus according to an embodiment of thepresent disclosure. The information processing apparatus 1 according tothe embodiment of the present disclosure includes at least a CPU(Central Processing Unit) 11, a memory 12, a storage device 13, an I/Ointerface 14, a video interface 15, a portable disc drive 16, acommunication interface 17, and an internal bus 18 interconnecting thehardware components given above.

The CPU 11 is connected to the hardware components of the informationprocessing apparatus 1 given above through the internal bus 18, controlsoperations of the hardware components given above, and executes varioussoftware functions according to a computer program 100 stored on thestorage device 13. The memory 12 is implemented by a volatile memorysuch as an SRAM or an SDRAM, in which a load module of the computerprogram 100 is loaded when the computer program 100 is executed andtemporary data generated during execution of the computer program 100 isstored.

The storage device 13 is implemented by a storage device such as abuilt-in fixed storage device (a hard disk) or ROM. The computer program100 stored in the storage device 13 has been downloaded from a portablestorage medium 90, such as a DVD or a CD-ROM, on which information suchas programs and data are stored, to the storage device 13 by theportable disc drive 16, and is loaded into the memory 12 from thestorage device 13 when the computer program 100 is executed. Thecomputer program 100 may be downloaded from an external computerconnected through the communication interface 17, of course.

The communication interface 17 is connected to the internal bus 18 andis capable of sending and receiving data to and from an externalcomputer or other device by connecting to an external network such asthe Internet, a LAN, or a WAN.

The I/O interface 14 is connected to input devices such as a keyboard 21and a mouse 22 and receives input data. The video interface 15 isconnected to a display device 23 such as a CRT display or aliquid-crystal display and displays a given image.

FIG. 2 is a functional block diagram of the information processingapparatus 1 according to the embodiment of the present disclosure. InFIG. 2, a subspace identifying unit 201 of the information processingapparatus 1 identifies T subspaces (T is a natural number) which arepart of a state space. Equation 1 is an equation written with a t-th (tis a natural number, t≦T) subspace (subspace t) and is solvable usingpolicy evaluation of a conventional method with a cyclic Markov decisionprocess.

$\begin{matrix}{{\begin{pmatrix}c_{1} \\c_{2} \\\vdots \\c_{T - 1} \\c_{T}\end{pmatrix} - {g\begin{pmatrix}1 \\1 \\\vdots \\1 \\1\end{pmatrix}} + {\begin{pmatrix}0 & P_{1,2} & 0 & \ldots & 0 \\\vdots & 0 & P_{2,3} & \ddots & \vdots \\\vdots & \; & \ddots & \ddots & 0 \\0 & \; & \; & \ddots & P_{{T - 1},T} \\P_{T,1} & 0 & \ldots & \ldots & 0\end{pmatrix}\begin{pmatrix}h_{1} \\h_{2} \\\vdots \\h_{T - 1} \\h_{T}\end{pmatrix}}} = \begin{pmatrix}h_{1} \\h_{2} \\\vdots \\h_{T - 1} \\h_{T}\end{pmatrix}} & \left( {{Equation}\mspace{14mu} 1} \right)\end{matrix}$

In Equation 1, a vector c_(t) (t=1, . . . , T) represents the cost ofeach state of subspace t among the T subspaces and the i-th component ofthe vector c_(t) represents the cost of the i-th state of subspace t.

A matrix P_(t, t+1) (t=1, . . . , T) represents the probability oftransition from each state of the subspace t to each state of thesubspace t+1. The i-, j-th entry of the matrix P_(t, t+1) represents theprobability that a transition is made from the i-th state of thesubspace t to the j-th state of the next subspace t+1. The matrixP_(T, T+1) is defined to be equal to the matrix P_(T, 1).

Here, g is a variable representing a gain. The term gain as used hereinrefers to an average gain of the policy per a Markov decision process. Avector h_(t) (t=1, . . . , T) is a variable representing a bias of eachstate of the subspace t. The bias is defined for each state. A bias froma given state represents the difference between a gain that can beobtained from the state in N operations and a gain gN that can beobtained from an average state in N operations, where N is asufficiently large number of operations. Let the vector h be defined asa vector including T vectors (h₁, h₂, . . . , h_(T)). Then, if thevector h is the solution to be found, vector h+k×vector 1 (where “vector1” is a vector whose components are all l, and k is an arbitraryconstant) is also a solution. Solving the equation (Equation 1) meansfinding the variable g and the vector h.

A selection unit 202 selects the t-th subspace among identifiedsubspaces. In this embodiment, the selection unit 202 selects of thet-th subspace that has the fewest states among the T subspaces. Forsimplicity of explanation, a case where t=1 will be taken as an examplein the following description.

A probability and cost computing unit 203 computes the probability of,and an expected value of the cost of, going from one or more states ofthe selected t-th subspace to one or more states of the t-th subspace inthe following cycle.

Equation 2 represents a matrix Q each of whose entries (i, j) is theprobability of transition from a selected state i of the subspace (thet-th subspace) to a state outside the selected subspace to a state j ofthe selected subspace. Equation 2 defines a matrix Q where transitionprobability matrices P, each of which is a transition probability matrixfrom one state to the next, are multiplied one after another.

Q≡P _(1,2) P _(2,3) . . . P _(T−1,T) P _(T,1)   (Equation 2)

Equation 3 defines a vector b each of whose i-th components is anexpected value of cost of transition from a state i of a selectedsubspace (the t-th subspace) to a state outside the selected subspace toany of the states of the selected subspace.

b≡c ₁ +P _(1,2) c ₂+(P _(1,2) P _(2,3))c₃+ . . . +(P _(1,2) P _(2,3) . .. P _(T−1,T))c _(T)   (Equation 3)

Then, the variable g representing gain and the vector h_(t) (t=1) whichis a variable representing the bias of each state of the subspace t arefound by using the matrix Q (Equation 2) each of whose entries (i, j) isthe probability of transition from the state i of the selected subspace(the t-th subspace) to a state outside the selected subspace to thestate j of the selected subspace, and the vector b each of whose icomponents is an expected value of cost of transition from the state iof the selected subspace (the t-th subspace) to a state outside theselected subspace to any of the states of the selected subspace, as thesolutions of the equation (Equation 4) given below.

$\begin{matrix}\left( {{I - {Q\left. {T\; 1} \right)\left( \frac{h_{1}}{g} \right)}} = b} \right. & \left( {{Equation}\mspace{14mu} 4} \right)\end{matrix}$

In Equation 4, the matrix I represents an identity matrix and the vector1 represents a vector whose components are all 1. The variable g, whichrepresents gain, and the vector h_(t) (t=1), which is a variablerepresenting the bias of each state of the subspace t can be found. Arecursive computing unit 204 recursively computes values and expectedvalues of costs in order from the (t—1)-th subspace on the basis of theprobability and the expected value of cost that are determined by thevariable g representing gain and the vector h_(t) (t=1), which is avariable representing the bias of each state of the subspace t.

Specifically, since t=1, the vector h_(t) (for t=T) is computed next andvectors are recursively computed one after another. That is, the vectorh_(t) (for t=T), which is the variable representing the bias of a stateof the subspace t (for t=T) can be found first and the vector h_(t) (fort=T−1), vector h_(t) (for t=T−2), . . . , vector h_(t) (for t=2) can befound recursively in sequence as shown in Equation 5.

$\begin{matrix}\begin{matrix}{h_{T} = {c_{T} - {g\; 1} + {P_{T,1}h_{1}}}} \\{h_{T - 1} = {c_{T - 1} - {g\; 1} + {P_{{T - 1},T}h_{T}}}} \\\vdots \\{h_{2} = {c_{2} - {g\; 1} + {P_{2,3}h_{3}}}}\end{matrix} & \left( {{Equation}\mspace{14mu} 5} \right)\end{matrix}$

In Equation 5, the vector 1 represents a vector whose components areall 1. The vector h_(t) of the subspace t that has the fewest states iscomputed first and a vector h_(t) for another subspace is recursivelycomputed from that vector h_(t), rather than directly finding thevectors h_(t) (t=, 1 . . . , T) by computing an inverse matrix as inEquation 1. Accordingly, the computational load can be significantlyreduced.

FIG. 3 is a flowchart of a process procedure performed by the CPU 11 ofthe information processing apparatus 1 according to the embodiment ofthe present disclosure. In FIG. 3, the CPU 11 of the informationprocessing apparatus 1 identifies T subspaces (T is a natural number)that are part of a state space (operation S301). The CPU 11 selects thet-th subspace (t is a natural number, t≦T) among the identifiedsubspaces (operation S302). In this embodiment, the CPU 11 selects thet-th subspace that has fewest states among the T subspaces.

The CPU 11 computes a transition probability matrix Q for theprobability of going from one or more states of the selected t-thsubspace to one or more states of the t-th subspace in the followingcycle (operation S303). The CPU 11 computes a vector b including, as ani-th component, an expected value of cost of going from the state i ofthe selected subspace (the t-th subspace) passing to a state outside theselected subspace to a state j of the selected subspace (operationS304).

The CPU 11 then uses the computed matrix Q and vector b to compute avariable g, which represents gain, and a vector h_(t) (t=1), which is avariable representing a bias of each state of the subspace t (operationS305). The CPU 11 uses the variable g representing gain and a vectorh_(t), which is a variable representing a bias of each state of thesubspace t, to compute a vector h_(t−1), which is a variablerepresenting a bias of each state of a subspace t−1 (operation S306). Itshould be noted that if t=1, the vector h_(t−1) computed is a vectorh₀=h_(T) because of the cyclic nature.

The CPU 11 determines whether or not the CPU 11 has computed the vectorh_(t+1) (t×1 ) (operation S307). This is done because at the point intime when computation of the vector h_(t+1) is completed, vectors h forall subspaces in one cycle have been recursively computed.

If the CPU 11 determines that the vector h_(t+1) has not been computed(operation S307: NO), the CPU 11 decrements the argument t of the vectorh by ‘1’ (operation S308) and returns to operation S306 to repeat theprocess described above. If the CPU 11 determines that the vectorh_(t+1) has been computed (operation S307: YES), the CPU 11 ends theprocess.

It should be noted that when the vector b that includes, as the i-thcomponent, an expected value of cost of going from a state i of theselected subspace (the t-th subspace) passing to a state outside theselected subspace to any of the states of the selected subspace isdefined, costs that can occur in the future may be discounted. That is,Equation 3 may be multiplied by a discount rate λ (0<λ<1) according to astate transition. Equation 6 defines the vector b that includes, as thei-th component, an expected value of cost of going from a state i of theselected subspace (the t-th subspace) passing to a state outside theselected subspace to any of the states of the selected subspace.

b≡c ₁ +λP _(1,2) c ₂+λ²(P _(1,2) P _(2,3))c ₃+ . . . +λ^(T−1)(P _(1,2) P_(2,3) . . . P _(T−1,T))c _(T)   (Equation 6)

In this case, a variable g representing gain and a vector h_(t)(t=1),which is a variable representing a bias of each state of the subspace t,are found as solutions of the equation (Equation 7) using a matrix Q(Equation 2) each of whose entries (i, j) is the probability oftransition from a state i of a selected subspace (the t-th subspace) toa state outside the selected subspace to a state j of the selectedsubspace and a vector b each of whose i components is an expected valueof cost of transition from a state i of a selected subspace (the t-thsubspace)passing to a state outside the selected subspace to any of thestates of the selected subspace.

(I−Q)h ₁ =b   (Equation 7)

In Equation 7, the matrix I represents an identity matrix. The variableg, which represents gain, and the vector h_(t) (t=1), which is avariable representing the bias of each state of the subspace t can befound. A recursive computing unit 204 recursively computes values andexpected values of costs in order from the (t−1)-th subspace on thebasis of the probability and the expected value of cost that aredetermined by the variable g representing gain and the vector h_(t)(t=1), which is a variable representing the bias of each state of thesubspace t.

Specifically, since t=1, the vector h_(t) (t=T) is computed next andvectors are recursively computed one after another. That is, the vectorh_(t) (t=T), which is the variable representing the bias of a state ofthe subspace t (t=T) can be found first and the vector h_(t) (t=T−1),vector h_(t) (t=T−2), . . . , vector h_(t) (t=2) can be foundrecursively in sequence as shown in Equation 8.

$\begin{matrix}\begin{matrix}{h_{T} = {c_{T} + {\lambda \; P_{T,1}h_{1}}}} \\{h_{T - 1} = {c_{T - 1} + {\lambda \; P_{{T - 1},T}h_{T}}}} \\\vdots \\{h_{2} = {c_{2} + {\lambda \; P_{2,3}h_{3}}}}\end{matrix} & \left( {{Equation}\mspace{14mu} 8} \right)\end{matrix}$

Unlike Equation 5, Equation 8 does not use the gain g. This is becausethe cost that will occur in the future has been discounted previouslyand therefore gain can be considered to be 0 (zero).

In this way, this embodiment enables a cyclic Markov decision process todetermine optimum policies for problems having large sizes and determineoptimum policies for problems that cannot be solved by conventionalalgorithms such as value iteration, policy iteration, and linearprogramming.

The gain determined in the embodiment described above is the averagegain g per state transition of the Markov decision process and it isassumed that the gain can be uniquely determined for all states.However, in real problems, the gain can vary from state to state. Ifthis is the case, the gain g is computed as a vector for each subspacet. That is, a gain vector g is computed as a value variable for each ofT subspaces (T is a natural number) to optimize a Markov decisionprocess.

Therefore, the variable vector g_(t) (t=1) representing gain and thevector h_(t) (t=1) which is a variable representing the bias of eachstate of the subspace t are found by using the matrix Q (Equation 2)each of whose entries (i, j) is the probability of transition from thestate i of the selected subspace (the t-th subspace) passing to a stateoutside the selected subspace to the state j of the selected subspace,and the vector b each of whose i components is an expected value of costof transition from the state i of the selected subspace (the t-thsubspace) passing to a state outside the selected subspace to any of thestates of the selected subspace, as the solutions of the equation(Equation 9) given below.

$\begin{matrix}\left( {{\frac{I - Q}{O}\left. \frac{TI}{I - Q} \right)\left( \frac{h_{1}}{g_{1}} \right)} = \left( \frac{b}{0} \right)} \right. & \left( {{Equation}\mspace{14mu} 9} \right)\end{matrix}$

In Equation 9, the matrix I represents an identity matrix. The variablevector g_(t) (t=1) representing gain and the vector h_(t) (t=1), whichis a variable representing the bias of each state of the subspace t canbe found. The recursive computing unit 204 recursively computes valuesand expected values of costs in order from the (t−1)-th subspace on thebasis of the probability and the expected value of cost that aredetermined by the variable vector g_(t) (t=1) representing gain and thevector h_(t) (t=1), which is a variable representing the bias of eachstate of the subspace t.

Specifically, since t=1, the vector g_(t) (t=T) is computed next andgain vectors are recursively computed in sequence. That is, a gainvector g_(t) (t=T) of the subspace t (t=T) can be computed first. Thegain vector g_(t) (t=T) is then used to compute a vector h_(t) (t=T).Then a gain vector g_(t) (t=T−1) and a vector h_(t) (t=T−1), and a gainvector g_(t) (t=T−2) and a vector h_(t) (t=T−2) are recursively computedin sequence. In this way, pairs of gain vector g_(t) and vector h_(t)can be recursively computed in sequence as shown in Equation 10.

$\begin{matrix}\begin{matrix}{g_{T} = {P_{T,1}g_{1}}} & {h_{T} = {c_{T} - g_{T} + {P_{T,1}h_{1}}}} \\{g_{T - 1} = {P_{{T - 1},T}g_{T}}} & {h_{T - 1} = {c_{T - 1} - g_{T - 1} + {P_{{T - 1},T}h_{T}}}} \\\vdots & \vdots \\{g_{2} = {P_{2,3}g_{3}}} & {h_{2} = {c_{2} - g_{2} + {P_{2,3}h_{3}}}}\end{matrix} & \left( {{Equation}\mspace{14mu} 10} \right)\end{matrix}$

FIG. 4 shows the results of the above-described algorithm applied toproblems that cannot be solved easily by conventional algorithms such asvalue iteration, policy iteration, and linear programming. FIG. 4 is atable comparing computational processing times required for determiningan optimum policy using a Markov decision process on the informationprocessing apparatus 1 according to the embodiment of the presentdisclosure.

In FIG. 4, the size of the problem to be solved is represented by thenumber of states multiplied by the number of state-action pairs. CPLEX,which is a so-called general-purpose optimization engine, can solve theproblems up to problem 4 but cannot solve problems of sizes larger thanproblem 5. Policy Iteration, which is a well-known algorithm, can solveproblems of larger sizes.

However, it takes a computational processing time of 20078 seconds, ornearly 5 hours, for the algorithm to solve problem 7, which is too longas the time for determining a policy in reality. In contrast, thealgorithm according to this embodiment can determine an optimum policyfor problem 7, which has the largest size, in a little more than threeminutes. It can be seen that the larger the size of the problem, thebigger the increase of the speed of computational processing is.Therefore, the method according to this embodiment can moresignificantly reduce computational load for solving larger size problemsand therefore can be applied to problems having larger sizes.

The above-described method according to this embodiment which moreefficiently determines an optimum policy than the exiting algorithms isapplicable to a power generation plan at an electric power utilitycompany. Assume, for example, a power generation plan to determine 15minutes in advance the amount of electric power to be generated in thenext 30 minutes and to determine the amount of electric power to becharged and discharged for an electric storage every 3 minutes. In thiscase, there will be 30 minutes/3 minutes=10 cycles, namely, T=10.

A state space is divided into subspaces, each of which represents the“state” at time t. A transition from a subspace at time t occurs only toa subspace at time (t +1). It is assumed here that the “state” isdefined by time t, the difference x between a planned amount of electricpower and the amount of electric power used, the amount of storedelectric power y, and the preset target amount of electric power z.

Time t is any of 1, 2, . . . , T, x represents the difference betweenthe planned amount of electric power and the amount of electric poweractually used from time 0 to time t within the cycle T, y represents theamount of electric power stored in the electric storage, and zrepresents the difference between the planned amount of electric powerdetermined at time t=5 and the estimated amount of electric power to beused during the next 30 minutes.

Using a Markov decision process for the model described above, anoptimum action for each state, for example the amount of electric powerto be charged and discharged during the next 3 minutes can bedetermined, and the amount of electric power to be generated during thenext 30 minutes can be determined at time t=5.

Matrix P_(t, t+1) in Equation 1 represents a matrix each of whoseentries is the probability of a transition from the state at time t tothe state at time t+1. The vector c_(t) represents the cost of eachstate at time t. The cost in this model represents a cost equivalent toelectric power loss during the next 3 minutes due to a chargingefficiency of less than 1 if the action is charging, or a cost thatoccurs according to the difference between the planned amount ofelectric power and the amount of electric power actually used, inaddition to the power loss cost due to charging when t=T. The cost thatoccurs according to the difference between the planed amount of electricpower and the amount of electric power actually used is for example thecost of purchasing additional electric power or a penalty due to surpluselectric power.

The present disclosure is not limited to the exemplary embodimentdescribed above. Various modifications and improvements can be madewithout departing from the spirit of the present disclosure. Forexample, while the values and expected values of costs of one or morestates of the t-th subspace are computed in the embodiment describedabove, the average of the values and the average of costs may becomputed as typical values.

1. A method for determining an optimum policy by using a Markov decisionprocess in which T subspaces each have at least one state having acyclic structure, comprising: identifying, with a processor, subspacesthat are part of a state space; selecting a t-th (t is a natural number,t≦T) subspace among the identified subspaces; computing a probabilityof, and an expected value of a cost of, reaching from one or more statesin the selected t-th subspace to one or more states in the t-th subspacein a following cycle; and recursively computing a value and an expectedvalue of a cost based on the computed probability and expected value ofthe cost, in a sequential manner starting from a (t−1)-th subspace. 2.The method according to claim 1, wherein selection of a subspace havingfewest states among the T subspaces is received as the t-th subspace. 3.The method according to claim 1, wherein an average value of values andan average value of expected values of cost of one or more states in thet-th subspace are computed.
 4. The method according to claim 1, whereina value variable for each of the T subspaces is computed to optimize theMarkov decision process.